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Article

Durum Wheat Field Performance and Stability in the Irrigated, Dry and Heat-Prone Environments of Sudan

by
Izzat S. A. Tahir
1,*,†,
Elfadil M. E. Elbashier
1,
Hala M. Mustafa
1,
Ashraf M. A. Elhashimi
2,
Modather G. A. Abdalla
3,
Mohamed K. Hassan
1,4,
Abu Sefyan I. Saad
1,
Awad A. E. Elbashir
3,4,
Omer Elsheikh
5 and
Sara Meheesi
1,2
1
Wheat Research Program, Gezira Research Station, Agricultural Research Corporation (ARC), Wad Medani P.O. Box 126, Sudan
2
Hudeiba Research Station, Agricultural Research Corporation (ARC), Ad-Damar P.O. Box 31, Sudan
3
Dongola Research Station, Agricultural Research Corporation (ARC), Dongola P.O. Box 35, Sudan
4
New Halfa Research Station, Agricultural Research Corporation (ARC), New Halfa P.O. Box 17, Sudan
5
New Hamdab Research Station, Agricultural Research Corporation (ARC), New Hamdab, Sudan
*
Author to whom correspondence should be addressed.
†
Current address: Arid Land Research Center, Tottori University, 1390 Hamasaka, Tottori 680-0001, Japan.
Agronomy 2023, 13(6), 1598; https://doi.org/10.3390/agronomy13061598
Submission received: 30 May 2023 / Revised: 9 June 2023 / Accepted: 10 June 2023 / Published: 13 June 2023
(This article belongs to the Section Crop Breeding and Genetics)
Browse Figures
Versions Notes

Abstract

:
Developing climate-resilient crop varieties with better performance under variable environments is essential to ensure food security in a changing climate. This process is significantly influenced, among other factors, by genotype × environment (G × E) interactions. With the objective of identifying high-yielding and stable genotypes, 20 elite durum wheat lines were evaluated in 24 environments (location–season combination) during 5 crop seasons (2010/11–2014/15). The REML (residual maximum likelihood)-predicted means of grain yield of 16 genotypes that were common across all environments ranged from 3522 kg/ha in G201 to 4132 kg/ha in G217. Results of additive main effect and multiplicative interaction (AMMI) analysis showed that genotypes (G), environments (E), and genotype × environment interaction (GEI) significantly affected grain yield. From the total sum of squares due to treatments (G + E + GEI), E attributed the highest proportion of the variation (90.0%), followed by GEI (8.7%) and G (1.3%). Based on the first four AMMI selections for grain yield in the 24 environments, genotypes G217, G219, G211, and G213 were selected in 23, 12, 11, and 9 environments, respectively. The genotype and genotype × environment biplot (GGE) biplot polygon view showed that the environments were separated into three mega-environments. The winning genotypes in these mega-environments were G217, G214, and G204. Genotypes G212, G220, G217, G215, and G213 showed low AMMI stability values (ASV), whereas genotypes G217, G220, G212, G211, and G219 showed low genotype selection index (GSI), indicating their better stability and adaptability to the test environments. The results indicated that genotypes G217, G219, G211, G213, and G220 combined both high grain yield and stability/adaptability under dry but irrigated and heat-prone environments. An in-depth analysis of the superior genotypes could help better understand the stress-adaptive traits that could be targeted to further increase durum wheat yield and stability under the changing climate.

1. Introduction

Durum wheat (Triticum turgidum L. var durum Desf.), a tetraploid species of wheat cultivated on a vast array of agro-ecologies, is one of the oldest cultivated cereals and is the 10th most important crop in the world with a global acreage of about 13.5 million ha in 2020 [1,2,3,4,5]. As a result of its use in semolina production and pasta making, durum wheat ranks second to bread wheat among the most important and widespread types of wheat in the world. Although it is grown in around 10% of the world’s wheat area, durum wheat always fetches higher prices in the world market than bread wheat [2].
Historically, the Mediterranean Basin represents the main durum wheat cultivation region, where approximately 60% of the total area is located [1]. However, recently, interest in the cultivation of durum wheat has developed in many areas around the world [4]. Improving durum wheat grain yield and quality is one of the main breeding goals in many countries due to the increased market demand for good-quality durum wheat. With such expansion, the crop is expected to be grown in less favorable environments under abiotic stresses such as drought and heat. Even within the traditional durum wheat areas, terminal heat stress is common and could escalate due to climate change, which might cause a reduction in the world’s suitable potential durum wheat cultivation area by 19% in 2050 [5,6,7]. Therefore, it is of paramount importance to investigate the performance of durum wheat under such environments. One of the strategies for breeding durum wheat could require employing appropriate breeding methodologies that ensure the pyramiding of desirable genes for yield and other desirable traits, followed by multi-location testing of progenies and elite lines to cope with changing and unpredictable climatic conditions [4,8,9].
Recently, interest in durum wheat has been increasing in Sudan; hence, its improvement is one of the objectives of the Wheat Research Program in Sudan. Sporadic and localized attempts were made in the 1990s to develop durum wheat varieties in Sudan through its introduction from international centers and regional programs, which resulted in the release of a single durum wheat variety (Cham-1) in 1996 [10]. Thereafter, in collaboration with ICARDA, CIMMYT, and ACSAD, hundreds of lines were introduced and tested under heat-stress environments in Sudan. The preliminary results indicated that a comparable grain yield to bread wheat could be achieved in the major wheat-growing areas in Sudan. Many promising heat-tolerant durum wheat lines have been identified, which encouraged testing them in multi-environment trials across the country. Therefore, a durum wheat national yield trial has been initiated to test the newly identified promising lines against the only durum wheat variety (Cham-1) released in Sudan.
The most cost-effective and environmentally safe way to meet the future durum wheat productivity challenges due to climate change is through both classical and modern breeding [8]. However, one of the major challenges that durum wheat improvement faces is genotype × environment interaction [4,9,11,12,13,14]. Information on the genotype × environment interaction of durum wheat under the heat stress conditions of Sudan, where a temperature gradient exists when moving from the relatively cooler areas in the north to the continuous heat stress areas in the central clay plain, is lacking. Knowledge of this interaction could be used to design an efficient breeding program and identify testing mega-environments to develop high-yielding and stable varieties for the semi-arid, irrigated, and heat-stressed environments of Sudan. Therefore, 20 durum wheat lines were tested in multi-environment trials with the objectives of identifying high-yielding and stable durum wheat genotypes in dry and heat-prone environments and finding mega-environments for future testing of durum wheat for further improvement. The study identified durum wheat genotypes that combined both high grain yield and stability/adaptability under dry, irrigated, and heat-prone environments. As a result, four durum wheat varieties were officially released for the heat-prone environments of Sudan [15].

2. Materials and Methods

2.1. Durum Wheat Genotypes

The durum wheat genotypes used in this study were selected from larger populations introduced from different sources, including ICARDA, CIMMYT, ACSAD, etc. Preliminary and advanced evaluation of these materials resulted in the identification of promising elite lines that were included in multi-environment yield trials during the 2010/11–2014/15 cropping seasons. Twenty genotypes (including the check variety, Cham-1) were evaluated in a range of environments representing a temperature gradient from relatively cooler locations in northern Sudan, through River Nile State, to the heat-stressed areas in Gezira and Kassala States.

2.2. Experimental Locations and Conditions

On-station and on-farm experiments were conducted during the 2010/11–2014/15 cropping seasons. Experimental locations were selected to represent major wheat-growing areas in Sudan. The locations used were Dongola Research Station (DN, 19°08′ N, 30°26′ E, 238 masl), Hamdab Research Station (HAM, 17°56′ N, 31°10′ E, 255 masl), Hudeiba Research Station (HD, 17°33′ N, 33°55′ E, 350 masl), New Halfa Research Station (NH, 15°22′ N, 35°35′ E, 455 masl), and Gezira Research Station (WM, 14°22′ N, 33°29′ E, 410 masl) in addition to on-farm trials conducted at Al-Kumur (KR, 14°18′ N, 33°22′ E. 412 masl) and Managil (ML, 14°21′ N, 22°02′ E, 405 masl) areas in the Gezira Scheme. The numerical suffix that follows the abbreviation of the location refers to the cropping season. For example, DN11 means Dongola season 2010/2011. The experiment was conducted for five seasons at DN, HD, NH, and WM, except at HD in 2014/15 (19 environments). In addition, the experiment was also conducted for two seasons at HAM (2013/14 and 2014/15), two seasons at KR (2012/13 and 2013/14), and one season at ML (2012/13).
The soils vary from sandy clay loam in the Northern State (Dongola and Hamdab) to clay loam, classified as a middle-terrace soil, in the River Nile State (Hudeiba) to heavy clay soil, known as black cotton soil (vertisols) in central Sudan (Wad Medani, Al-Kumur, Al-Managil, and New Halfa). All soils are alkaline (pH 8.0–8.5) with low organic matter content and low levels of nitrogen and phosphorus. In Sudan, the growing season (November–March) is dry, with no rains and low relative humidity (always in the range of 25–35% during the season), except in the New Halfa area. Thus, almost all wheat areas are fully irrigated.
Each genotype was seeded mechanically or manually (depending on location) in plots consisting of eight rows, 6 m long and 0.2 m apart. The seed rate used was about 135 kg/ha. Before sowing, seed dressing was performed with an insecticide/fungicide mixture of Gaucho (Imidacloprid 35% WP) and Raxil (Pencycuron 7.5% WP) at a rate of 0.5 and 1.25 g/kg of seed, respectively. The plots were fertilized using DAP (Diammonium phosphate) or triple superphosphate by furrow placement prior to planting at a rate of 43 kg/ha of P2O5, whereas urea was split-applied by broadcasting before the second and fourth irrigations at a rate of 86 kg/ha of N. The plots were irrigated every 10–14 days to avoid any water-deficit stress. The plots were kept free of weeds with manual weeding at least twice. Generally, the standard cultural practices recommended by the Agricultural Research Corporation for wheat production were used. Except for the New Halfa area, all locations are free from major wheat diseases, including rusts. Even at New Halfa, no serious disease infections were observed in the trials conducted there. At all locations, including the on-farm trials, an alpha lattice design with three replications was used.

2.3. Data Collection

Data on phenology, plant height, grain yield, and yield component traits were collected from different experimental locations, except for HAM and the two on-station locations, where only the grain yield data were collected. Days to heading were taken when 50% of the spikes merged from the leaf sheath, whereas days to maturity were recorded as the number of days from sowing to 90% of spikes’ glumes losing green color. Growing degree days (GDD) were calculated considering TBase = 10 °C and following Method 1, suggested by Mcmaster and Wilhelm [16]. Nevertheless, the TBase was always lower than the mean temperature. At physiological maturity, plant height (cm) was measured from the ground level to the tip of the spike (excluding awns). A sample of 10 spikes was taken from the central rows, where the number of grains spike−1 and thousand kernel weight were determined. A net area of 6.0 m2 (six rows by 5 m) was hand harvested from the ground level. The harvested material was bundled and left to sun-dry. The bundles were weighed to obtain the biomass, then threshed mechanically or manually, and the grains were weighed to obtain the grain yield.

2.4. Statistical Analysis

The statistical analysis for grain yield was conducted for each location in each season to test significant differences among lines. Each location–season combination was regarded as an environment. The best linear unbiased estimates (BLUEs) of grain yield for each environment and genotype were obtained using the mixed models theory using the method of residual maximum likelihood (REML). The genotypes (G), environments (E), and their interaction (GEI) were treated as fixed effects, whereas replications nested within the environment were assigned to the random model. Grain yield was analyzed, along with other traits, using data from only 16 genotypes and four on-station locations, namely Dongola, Hudeiba, New Halfa, and Wad Medani. In this case, genotype, location, season, and their interactions were considered fixed effects. The stress tolerance index (STI) was calculated for all traits considering Dongola as the non-stress environment and Wad Medani as the heat-stress environment, following the formula of Fernandez [17].
The additive main effect and multiplicative interaction (AMMI) model was used to partition genotype × environment (GE) interaction [18]. The proportion of the sum of squares (SS) of the GEI explained by each IPCA determined the number of bilinear terms retained in the AMMI analysis. Following AMMI analysis, two stability measures were calculated, AMMI stability value (ASV) [19] and genotype selection index (GSI) [20].
Genotype and genotype × environment (GGE) biplot analysis [21] was used to identify superior genotypes (which won where) and further ascertain grain yield stability. The grain yield from the experimental stations (21 environments) was used in the GGE analysis. All statistical analyses were carried out using Genstat Twenty-second Edition (VSN International Ltd.) [22].

3. Results

3.1. Effect of Environment on Grain Yield

Table 1 summarizes the maximum and minimum temperatures, the number of days with temperature ≥ 35 °C, and the number of days with temperature ≤ 15 °C at four experimental stations during the crop growth cycle (November–March) in seasons 2010/2011–2014/2015. Maximum temperature up to 44.7 °C was recorded at Wad Medani, whereas the lowest temperature (2.9 °C) was recorded at Dongola in season 2014/15. The number of days with temperature ≥ 35 °C varied among the four locations and across seasons. Dongola always recorded the lowest number, followed by Hudeiba, whereas New Halfa recorded the highest number across all five seasons, followed by Wad Medani (Table 1). In contrast, Dongola recorded the highest number of days with temperature ≤ 15 °C, whereas New Halfa recorded the lowest number across all seasons. Wad Medani was always second to New Halfa in three seasons for temperature ≤ 15 °C and recorded the same number of days as Hudeiba in seasons 2010/11 and 2014/15.
The mean temperature during the growing season (November–March) was always higher than 20 °C and ranged from 20.8 °C at Dongola season 2021/12 to 27.5 °C at New Halfa and Wad Medani in seasons 2012/13 and 2014/15, respectively (Table 1). The growing degree days (GDD) to mean time of heading of all genotypes also increased with increased mean temperature and varied from 632.7 °C in Dongola in season 2011/12 to 964.4 °C in Wad Medani in season 2013/14.
Considerable differences were found among the 24 environments in grain yield and other traits. The REML-predicted means of grain yield in the 24 environments ranged from 2253 kg/ha at HD13 to 6347 kg/ha at DN12 (Supplementary Table S1). A strong negative relationship (R2 = 0.575, p < 0.001) was found between the grain yield at four experimental stations and the number of days with temperature ≥ 35 °C (Figure 1). On the other hand, a stronger and more positive relationship (R2 = 0.852, p < 0.001) was found between grain yield at the four stations and the number of days with temperature ≤ 15 °C.
A direct comparison of the mean performance of all genotypes at Wad Medani relative to Dongola showed a reduction of 38.0% in grain yield. This reduction in grain yield corresponded with a 35.7% reduction in biomass. Other traits were also reduced but with much lower percentages. For instance, the reductions in plant height, grains spike−1, thousand-kernel weight, days to heading, days to maturity, and harvest index were 15.3, 13.2, 8.3, 7.7, 6.9, and 4.4%, respectively.
The REML-predicted means of grain yield of the 16 common genotypes across all environments ranged from 3522 kg/ha in G201 (Cham-1, the check variety) to 4132 kg/ha in G217 (SNITAN*2/RBC) (Table 2). All the new genotypes outyielded the check variety. The percent increase over the grain yield of the check variety ranged from 2 to 17 %. Genotypes G217, G219, and G211 exceeded the grain yield of the check variety by more than 10%. Both G218 and G220 showed an increase of 9% over the check, whereas both G213 and G214 had grain yields of 8% higher than that of the check (Table 2).

3.2. Grain Yield-Related Traits

The effect of location, genotype, and their interaction on biomass were all significant. Averaged across genotypes, the biomass varied from 14,227 kg/ha at Dongola to 9155 kg/ha at Wad Medani (Figure 2a, Supplementary Table S2). Biomass showed reductions of 31.1, 30.7, and 35.7% at Hudeiba, New Halfa, and Wad Medani, respectively, compared to Dongola. The STI for biomass varied from 0.76 in G220 to 0.55 in G201, which reflected the high reduction percentage in biomass at Wad Medani compared to Dongola.
Highly significant differences were found among genotypes for the harvest index. Similarly, the location × genotype interaction effect was highly significant; however, the location was not significant (Figure 2b, Supplementary Table S2). The STI for the harvest index ranged from 1.05 in G211 to 0.87 in G203.
The number of grains spike−1 was significantly affected by location and genotype but not by their interaction. Across genotypes, the highest number of grains spike−1 was recorded at Dongola, whereas the lowest was found at New Halfa (Figure 2c, Supplementary Table S2). The highest STI for grains spike−1 was recorded by G220, followed by G215 and G206, whereas the lowest STI was recorded by G214.
Although the location showed no significant effect on thousand-kernel weight, both genotypes and the location × genotype interaction significantly affected the trait (Figure 2d, Supplementary Table S2). The STI for TKW varied from 1.09 in G211 to 0.65 in G220.

3.3. Phenology and Morphology

Location, genotype, and their interaction exhibited significant effects on days to heading of the tested genotypes. The high temperature resulted in the reduction of mean days to heading for all genotypes from 67 days at Dongola to 64 days at Hudeiba and to 61 days at Wad Medani and New Halfa (Figure 2e, Supplementary Table S2). The STI ranged from 1.10 for G220 to 0.82 for G201.
Significant differences were found among the tested genotypes for days to maturity due to location, genotype, and their interaction. The high temperature reduced the mean days to maturity of all genotypes from 104 days at Dongola to 93 days at New Halfa (Supplementary Table S2). Similar to DH, G220 exhibited the highest STI (1.02), whereas G201 showed the lowest STI value (0.85) for days to maturity.
Genotype, location, and their interaction significantly affected the plant height of the tested genotypes. The mean plant height decreased from 85 cm at Dongola to 73, 72, and 71 cm at New Halfa, Wad Medani, and Hudeiba, respectively (Figure 2f, Supplementary Table S2). The STI for plant height ranged from 0.94 in G203 to 0.76 in G201.

3.4. Trait Association

The mean grain yield across all environments significantly correlated with biomass and tended to correlate with the harvest index (P = 0.0532). No significant correlations were found between grain yield and both thousand-grain weight and number of grains spike−1 (Figure 3a). On the other hand, thousand-kernel weight significantly and negatively correlated with days to heading, days to maturity, and number of grains spike−1. High biomass production was associated with late heading, late maturity, and taller plants. Grain yield showed strong partial correlations with biomass (0.985) and harvest index (0.972) but not with grains spike−1 and thousand-kernel weight (Supplementary Figure S1a).
The stress tolerance index (STI) for grain yield was significantly associated with that of biomass, days to heading, and days to maturity (Figure 3b). The STI for biomass was significantly correlated with that of days to heading, days to maturity, and plant height. The stress tolerance index for grain yield showed strong partial correlations with that of biomass (0.994) and harvest index (0.989), whereas the partial correlation with STI of grains spike−1 was negative (−0.680) (Supplementary Figure S1b).

3.5. Genotype × Environment Interaction and the Stability of the Grain Yield

Results of AMMI analysis showed that genotypes (G), environments (E), and genotype × environment interaction (GEI) significantly (p < 0.01) affected grain yield. From the total sum of squares due to treatments (G + E + GEI), the environment attributed the highest proportion of the variation (90.0%), followed by GEI (8.7%), whereas G contributed only 1.3% of the total variation. Further partitioning of the total variance due to GEI showed that the first four terms were all significant (Table 3). The first, second, third, and fourth principal component analysis scores (IPCA1, IPCA2, IPCA3, and IPCA4) accounted for 20.6, 16.4, 12.3, and 11.4%, respectively. Based on the first four AMMI selections per environment for grain yield, genotypes G217, G219, G211, and G213 have been selected in 23, 12, 11, and 9 environments, respectively, compared to the check variety (G201) that has been selected in only 1 environment (Table 4).
The AMMI analysis was also conducted for biomass, harvest index, days to heading, days to maturity, and plant height (Supplementary Table S3). The analysis revealed that G, E, and GEI significantly affected all studied traits but to varying degrees. The environment has the highest proportion of variation and contributed by 89.5, 86.7, 64.0, 71.9, and 76.8% of the total variation due to treatment for biomass, harvest index, days to heading, days to maturity, and plant height, respectively. The effect of genotype varied from 2.3% in biomass to 22.2% in days to heading, whereas the GEI effect varied from 8.2% in biomass to 16.7% in days to maturity (Supplementary Table S3).
Furthermore, a two-dimensional GGE biplot was constructed for the on-station environments using the first two principal components (PC1 and PC2). The GGE biplot analysis showed that PC1 and PC2 accounted for 41.77% of the variation, of which 21.59% was explained by PC1, whereas 20.17% was accounted for by PC2 (Figure 4). The GGE biplot polygon view showed that the environments were separated into five mega-environments. The first mega-environment included 10 out of 21 environments used in the analysis. The second, third, and fourth mega-environments included 4, 3, and 3 environments, respectively. The fifth mega-environment included only one location (HAM15) (Figure 4a). The best-performing genotype in the first mega-environment was G217, with G213 and G211 also showing good performance within the environments of this mega-environment. On the other hand, G204 was the winning genotype in the second mega-environment, whereas G219 and G214 were the winning genotypes in the third and fourth mega-environments, respectively (Figure 4).
Based on the ranking biplot for comparison of environments, the best six environments were HD13, HD12, WM11, WM15, WM13, and DN15, respectively, all of them clustered together in the largest mega-environment (Figure 4b). Likewise, the ranking biplot for comparison of the genotypes revealed that G217, G211, G213, and G219 were the best four genotypes, respectively, and were clustered into two mega-environments (Figure 4c).
Both AMMI stability values (ASV) and genotype selection index (GSI) were used to evaluate the stability and adaptability of the durum wheat genotypes to the environments used in this study. The ASV for grain yield varied from 4.7 in G212 to 37.7 in G214. The GSI ranged from 4 in G217 to 29 in G209 (Table 1). Genotypes G212, G220, G217, G215, and G213 showed low AMMI stability values, whereas genotypes G217, G220, G212, G211, and G219 showed low genotype selection index.

4. Discussion

The agronomic performance and GEI of durum wheat were tested across 24 dry, heat-stressed, irrigated environments. The study showed that genotype, environment, and genotype × environment interaction were significant for most of the traits. The differences among the environments were clear to the extent that the variance due to environment in grain yield counted for more than 90%. The significant GEI clearly indicated the differential responses of the genotypes to different environments. The environments covered almost all major wheat-producing areas in Sudan and ranged from the relatively cool environment in northern Sudan to the continuous heat-stressed environments in central and eastern Sudan. The environments where this study was conducted represent one of the hottest wheat-growing environments in the world [23]. During the growing season (November–March), the number of days with temperature ≥ 35 °C varied from less than 20 days in the relatively cool areas in the north to more than 90 days in the continuous heat-stressed areas in central Sudan. On the other hand, the number of days with temperatures ≤ 15 °C was always more than 70 days in the north and less than 50 days in central Sudan. The field performance of durum wheat under such ranges of environments has been rarely studied and reported. The temperature gradients at the experimental locations in this study provide a better understanding of how durum wheat genotypes interact with the environment. Therefore, in the face of climate change and global warming, the results obtained here could be of significant importance for future durum wheat production worldwide. The grain yield of durum wheat was negatively correlated with the number of days with temperatures ≥ 35 °C; however, the correlation with the number of days with temperatures ≤ 15 °C was positive, indicating the importance of the night temperature in the performance of durum wheat. The GDDs to mean heading time of all genotypes at WM and NH were higher than that at DN. Averaged across the five seasons, a 1% increase in GDD to mean heading time of all genotypes at WM compared to that at DN resulted in a 1% decrease in grain yield of all genotypes at WM compared to DN.
For all traits measured, including grain yield, the effect of the environment showed a much higher contribution to the total variation than that of the genotype and genotype × environment interaction (GEI). The high contribution of the environment to the total variation might be due to the wide range of environments used in contrast to the genotypes, which were all elite lines, in addition to the type of the traits and their mode of inheritance. The effect of the genotype was higher in days to heading (22.2%) and plant height (12.5%) than that of the GEI (13.8 and 10.7%, respectively). On the other hand, the effect of GEI showed a higher contribution to the total variation in days to maturity (16.7%), biomass (7.3%), and harvest index (7.8%) as compared to genotypic effects (11.4, 2.1, and 1.8%, respectively).
The variation in grain yield among test environments was much higher than the variation among the studied genotypes. The grain yield among the test environments ranged from 6347 kg/ha at Dongola in season 2011/12 to 2253 kg/ha at Hudeiba in season 2012/13, which represents a difference of more than 2.8 times. The higher contribution of environment and GEI to variation in grain yield was reported in durum wheat [4,9,14,24,25,26] and other crops such as rice, maize, sorghum, and soybean [27,28,29,30,31]. Durum wheat genotypes responded differently to the environmental conditions, as indicated by the significant effect of GEI on grain yield and other traits. The higher contribution of GEI to variation in grain yield compared to that of the genotype indicates the existence of different mega-environments. This means that the durum wheat genotypes studied here have different adaptations to the test environments. Thus, the identification and selection of environment-specific genotypes are necessary. However, wide adaptation to most of the test environments was also found, as in the case of G217, which has been selected as one of the best four genotypes in 23 out of 24 environments.
The positive association between grain yield and biomass, together with the similar reductions in both traits under heat stress, indicated the importance of the accumulation of sufficient biomass for better grain yield under heat stress conditions. Compared to the relatively cool environment at DN, heat stress resulted in 31 and 35% reductions in biomass at HD and WM, respectively. These reductions in biomass were associated with about 38% reduction in grain yield at HD and WM compared to DN. In the same manner, the stress tolerance index for grain yield correlated with that of biomass. The association of high biomass production with late heading, late maturity, and taller plants, coupled with the association of biomass STI with that of days to heading, days to maturity, and plant height in such selected elite lines, reinforced the importance of assimilating accumulation for better grain yield and confirmed the importance of maximized biomass production under the short cycle of bread wheat in these warm environments [32]. Neither thousand-kernel weight nor grains spike−1 correlated with grain yield. A negative correlation was reported between grain yield and thousand-kernel weight, possibly due to the short crop cycle and the resulting short grain filling duration [32]. The correlation between the grain yield and the harvest index was not strong; however, a strong partial correlation was found between the two traits when the effects of other traits were controlled. Harvest index is an important trait to be considered for further improvement under heat stress conditions as it was found to be associated with the genetic gain in grain yield in these environments [33]. An in-depth analysis of the traits associated with the superior performance of durum wheat genotypes under continuous heat stress conditions could help better understand the stress-adaptive traits that could be targeted to further increase durum wheat yield and stability in a changing climate. For better genetic gain, breeders need to strike a balance between evaluating the genotypes in a large number of environments with the associated high cost and testing in representative fewer environments (mega-environments) to reduce the cost of evaluation [21]. Five mega-environments were identified by the GGE biplot in this study. The clustering of the environments showed that the same location clustered into different mega-environments depending on the season. Moreover, two consecutive seasons within the same location mostly clustered into different mega-environments. This indicates that the season effect was more important than location. The effects of location and season could be partially due to the differences in temperature among the testing locations.
The large effect of the environment will need data collection from multi-environment trials so that stable genotypes with wide adaptation can be identified. The future breeding strategy should consider the seasonal variation within the same location; hence, testing for multiple seasons in multiple locations will be necessary. Likewise, the selection of genotypes with specific adaptations to specific environments could be achieved. The results of the comparison of different environments revealed that HD, WM, DN, and NH, in certain seasons, were capable of identifying superior genotypes. These four locations represent the major research station for wheat breeding and germplasm evaluation. However, multiple season evaluation at WM seems to be useful in discriminating durum wheat genotypes according to their yield performance. The HM15 environment clustered alone, and even differed from HM14, indicating again the large seasonal variation within the same location.
The relative genetic gain rate in grain yield of bread wheat under the heat-stressed environment of Sudan during the period from 1960 to 2006 was found to be 0.48% year−1 [33]. There is evidence that the rate of genetic gain has slowed down in recent years, which imposes a huge challenge for securing food under the circumstances of continuous demand for more wheat and ongoing climate change. Although the history of durum wheat breeding is recent, it is expected that the same situation might be true as has been reported elsewhere [34,35,36]. To overcome these challenges, integrated approaches will be necessary that combine broadening genetic diversity and application of modern breeding approaches considering genotypic (G), environmental (E), management factors (M), and their interactions by researchers, including breeders, agronomists, physiologists, pathologists, crop modelers, etc. [8,37,38,39]. From the breeding side, attempts are being made to harness the diversity of wheat’s wild relatives, including wild emmer wheat, to introduce genes for wheat’s genetic improvement to increase wheat resilience to cope with the effects of climate change [40,41,42,43,44,45]. A good example of global collaboration in durum wheat improvement is the Global Durum Wheat Panel (GDP). The GDP was designed to represent broad and diverse collections that capture 94–97% of the readily exploitable genetic diversity and provide a rapid means to identify and exchange useful alleles worldwide by dissecting these genetic diversities to meet pre-breeding and breeding goals [13].

5. Conclusions

The identification of different mega-environments, the presence of clear GEI, and the identification of superior genotypes in specific mega-environments reinforced an earlier report that suggested the identification of environment-specific genotypes without disregarding breeding for wide adaptation [46].
Multi-location tests across 24 environments over five cropping seasons prominently demonstrated the yield superiority of almost all newly tested lines over the released variety (Cham-1). Based on the AMMI selections per environment for grain yield as well as GGE biplot analysis, genotypes G217, G219, G211, and G213 have been identified as better adapted to the majority of the heat-stress environments of Sudan.
The field performance of durum wheat under continuous heat stress conditions (more than 70% of the days during the crop cycle showed temperatures higher than 35 °C in some areas and seasons) is reported here. As today’s environmental conditions, mainly high temperatures, in the areas of this study might represent tomorrow’s environment in many durum wheat production areas, especially in the Mediterranean area, it is anticipated that the results obtained here could be useful in understanding the future adaptation to climate change.

6. Patents

Four durum wheat varieties were officially released for commercial production in the heat-stress environments of Sudan as a result of this study. The varieties are Argu (G217), Zaidab (G219), Basatna (G213), and Wedelbur (G211), all owned by the Agricultural Research Corporation of Sudan [15].

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy13061598/s1. Table S1: REML-predicted means of grain yield (kg/ha) of 16 durum wheat genotypes grown across 24 environments during 2010/11–2014/15; Table S2: Grain yield, biomass, harvest index, thousand-kernel weight, number of grains per spike, days to heading, days to maturity, plant height and their stress tolerance indices (STI) for durum wheat grown at four locations during 2010/11–2014/15; Table S3: ANOVA of additive main effects and multiplicative interaction (AMMI) for biomass, harvest index, days to heading, days to maturity, and plant height of durum wheat genotypes in 19 environments for five seasons (2010/2011–2014/15); Figure S1. Partial correlation between each pair of traits after adjusting for all the other traits.

Author Contributions

Conceptualization, I.S.A.T., E.M.E.E., H.M.M. and A.M.A.E.; methodology, I.S.A.T., M.G.A.A., M.K.H., A.S.I.S., A.A.E.E., O.E. and S.M.; software, I.S.A.T.; validation, I.S.A.T., E.M.E.E., H.M.M. and A.M.A.E.; formal analysis, I.S.A.T.; investigation and data curation, A.M.A.E., M.G.A.A., M.K.H., A.S.I.S., A.A.E.E., O.E. and S.M.; writing—original draft preparation, I.S.A.T.; writing—review and editing, E.M.E.E., H.M.M., A.M.A.E., M.G.A.A., M.K.H., A.S.I.S., A.A.E.E., O.E. and S.M.; project administration, I.S.A.T.; funding acquisition, I.S.A.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was partially funded by the African Development Bank (AfDB) (grant no. 2100155022217) through the project Support to Agricultural Research for Development of Strategic Crops (SARD-SC) in Africa (SARD-SC Wheat Sub-project).

Data Availability Statement

All data are provided in tables, figures, and supplementary materials.

Acknowledgments

The authors would like to thank all technical and supporting staff who assisted in conducting the experiments and collecting the data at different on-station and on-farm locations. The authors are grateful to the farmers who provided the land for conducting on-farm trials.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Figure 1. Relationship of grain yield with number of days with temperature ≥ 35 °C and number of days with temperature ≤ 15 °C in 19 environments. ***: Significant at p < 0.001.
Figure 1. Relationship of grain yield with number of days with temperature ≥ 35 °C and number of days with temperature ≤ 15 °C in 19 environments. ***: Significant at p < 0.001.
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Figure 2. Box plot representation of durum wheat genotypes performance for (a) biomass, (b) harvest index, (c) number of grains spike−1, (d) thousand-kernel weight, (e) days to heading, and (f) plant height of 16 genotypes grown across four locations for five seasons. The box shows median, 1st and 3rd quartiles, and the 10 and 90 percentiles. DN = Dongola, HD = Hudeiba, NH = New Halfa, WM = Wad Medani.
Figure 2. Box plot representation of durum wheat genotypes performance for (a) biomass, (b) harvest index, (c) number of grains spike−1, (d) thousand-kernel weight, (e) days to heading, and (f) plant height of 16 genotypes grown across four locations for five seasons. The box shows median, 1st and 3rd quartiles, and the 10 and 90 percentiles. DN = Dongola, HD = Hudeiba, NH = New Halfa, WM = Wad Medani.
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Figure 3. Correlation plots (heatmaps) of grain yield and other traits in durum wheat grown across 19 environments. (a) Grain yield with other traits studied, (b) stress tolerance index (STI) of grain yield with that of other traits. Red and blue colors indicate positive and negative correlations, respectively. Numbers within the cells are correlation coefficients. GY = Grain yield; BIO = Biomass; HI = Harvest index; TKW = Thousand-kernel weight; GPS = Grains spike−1; DH = Days to heading; DM = Days to maturity; PH = Plant height.
Figure 3. Correlation plots (heatmaps) of grain yield and other traits in durum wheat grown across 19 environments. (a) Grain yield with other traits studied, (b) stress tolerance index (STI) of grain yield with that of other traits. Red and blue colors indicate positive and negative correlations, respectively. Numbers within the cells are correlation coefficients. GY = Grain yield; BIO = Biomass; HI = Harvest index; TKW = Thousand-kernel weight; GPS = Grains spike−1; DH = Days to heading; DM = Days to maturity; PH = Plant height.
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Figure 4. GGE biplot for grain yield of 16 durum wheat genotypes at 21 on-station environments in Sudan. Scattered plot for mega-environments (a), ranking biplot for comparison of environments (b) and genotypes (c). For environment and genotype codes, see Table 1 and Table 4.
Figure 4. GGE biplot for grain yield of 16 durum wheat genotypes at 21 on-station environments in Sudan. Scattered plot for mega-environments (a), ranking biplot for comparison of environments (b) and genotypes (c). For environment and genotype codes, see Table 1 and Table 4.
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Table
Table 1. Maximum, minimum, and mean temperatures, number of days with temperature ≥ 35 °C, number of days with temperature ≤ 15 °C, and growing degree days (GDD) to mean heating time at four experimental stations during 2010/2011–2014/2015.
Table 1. Maximum, minimum, and mean temperatures, number of days with temperature ≥ 35 °C, number of days with temperature ≤ 15 °C, and growing degree days (GDD) to mean heating time at four experimental stations during 2010/2011–2014/2015.
SeasonExperimental StationMaximum Temperature °CDays with Temperature ≥ 35 °CMinimum Temperature °CDays with Temperature ≤ 15 °CMean Temperature (Nov-Mar) °CGDD to Mean Heading Time (TBase = 10 °C)
2010/2011DN38.8166.68823.0675.7
HD39.5468.44724.9816.0
WM42.2967.24726.7890.6
NH40.89810.63326.7896.5
2011/2012DN43.0154.013120.8632.7
HD41.7339.05924.0774.6
WM44.7798.24226.8898.0
NH43.89211.83526.5885.2
2012/2013DN42.1475.28923.1688.3
HD41.5619.03625.5838.7
WM43.5848.63527.1870.7
NH43.511410.02027.5918.2
2013/2014DN43.5426.28122.9644.5
HD42.06110.05024.9831.7
WM44.0798.63827.1964.4
NH42.5938.53726.8834.5
2014/2015DN44.5422.97423.2647.5
HD44.5766.73925.7843.6
WM44.2954.83927.5856.8
NH43.61168.03027.3866.1
DN = Dongola, HD = Hudeiba, NH = New Halfa, WM = Wad Medani, TBase = base temperature.
Table
Table 2. Best linear unbiased estimates (BLUEs) of grain yield (kg/ha), AMMI stability value (ASV), and genotype selection index (GSI) of durum wheat genotypes grown across 24 environments during 2010/11–2014/15 cropping seasons.
Table 2. Best linear unbiased estimates (BLUEs) of grain yield (kg/ha), AMMI stability value (ASV), and genotype selection index (GSI) of durum wheat genotypes grown across 24 environments during 2010/11–2014/15 cropping seasons.
Genotype CodeName/CrossPedigree/Selection HistorySourceBLUPs (kg/ha)% of CheckASVGSI
G201CHAM-1CheckReleased variety352210025.026
G203Beltagy-2ICD97-0396-T-1AP-AP-5AP-0AP-16AP-AP-0SDNICARDA377110718.516
G204Gcn/4/D68-1-93A-1A//Ruff/Fg/3/Mtl-5ICD95-1302-C-3AP-0AP-1AP-0AP-5AP-AP-3AP-0AP-0SDNICARDA376010728.524
G206Msbl-1//Krf/HcnICD95-1133-T-0AP-1AP-0AP-3AP-0TR-2AP-AP-0SDNICARDA359910214.922
G208Aghrass-1/Bezaiz98-1ICD00-0018-T-21AP-AP-4AP-TR-0SDNICARDA373010626.424
G209Maamouri-2/CI115/5/F4 13 J.S/3/Arthur71/Lahn//Blk2/Lahn/4/QuarmalICD02-0601-T-TR-7AP-0TR-0SDNICARDA371910635.129
G210DCD DW 7/Ter-1ICD01-0039-T-14AP-TR-9AP-0AP-0SDNICARDA375210726.524
G211SU-ORDEGL3/3/Ch5/20048Traikia (Mor)//STJ3ACSAD-8835-27IZ-3IZ-2IZ-0IZ-0SDNACSAD395211213.29
G212Mrf1/Stj2//Bcrch1ICD99-0027-C-0AP-2AP-AP-5AP-AP-0SDNICARDA37681074.711
G213Ter-1//Mrf1/Stj2ICD99-0866-C-0AP-5AP-AP-4AP-AP-0SDNICARDA38131088.811
G214Ter-1/3/Stj3//Bcr/Lks4ICD99-1036-T-0AP-8AP-AP-9AP-AP-0SDNICARDA379710837.723
G215TARRONAIRAN37711078.213
G217SNITAN*2/RBCCGSS01B00031T-099Y-099B-099B-1Y-0B-1Y-0B-0SDNCIMMYT41321176.94
G218P91.272.3.1/3*MEXI75/3/2*STOT//ALTAR 84/ALDCGSS02Y00103T-099B-099B-52Y-0B-1Y-0B-0SDNCIMMYT383210930.719
G219CBC 509 CHILE/5/2*AJAIA_16 //HORA/JRO/3/GAN/4/ZARCDSS02Y01222T-0TOPB-0Y-0M-1Y-0Y-0SDNCIMMYT395711220.211
G220HAI-OU_17/PLATA_2//LIRO_3/3/RYPS27_3/SKARV_3CDSS02Y00294S-0Y-0M-25Y-0Y-0SDNCIMMYT38351095.26
Mean 3794
SE± 71.3
Backcrosses are designated with (*).
Table
Table 3. ANOVA of additive main effects and multiplicative interaction (AMMI) for grain yield (kg/ha) of 16 durum wheat genotypes in 24 environments for five seasons (2010/2011–2014/15).
Table 3. ANOVA of additive main effects and multiplicative interaction (AMMI) for grain yield (kg/ha) of 16 durum wheat genotypes in 24 environments for five seasons (2010/2011–2014/15).
Source of VariationDFSSMSFPercent Explained
Genotypes (G)152123354214155694.79 ***1.3
Environments (E)2315153892116588648746.25 ***90.0
Block486837625114245054.82 ***
GE Interactions3451468731044257191.44 ***8.7
IPCA137301825648157452.76 ***20.6
IPCA235240666026876172.32 ***16.4
IPCA333180136625458691.85 **12.3
IPCA431167655485408241.83 **11.4
Residuals209578447282767690.94 NS
Error720212985199295813
Total115119648573071707087
** and ***: Significant at p < 0.01 and 0.001, respectively. NS = Not significant. DF = Degree of freedom, SS = Sum of squares, MS = Mean squares, F = F value.
Table
Table 4. The best four genotypes in each environment according to AMMI selections.
Table 4. The best four genotypes in each environment according to AMMI selections.
NumberEnvironment §Estimated Yield (kg/ha)ScoreBest Four Genotypes
1st2nd3rd4th
1DN116057−8.939G217G214G219G209
2DN1263477.292G214G210G217G219
3DN13534411.041G219G217G213G204
4DN1452448.359G204G220G203G217
5DN154205−6.192G218G217G219G209
6HM1459991.854G210G214G217G211
7HM153707−22.171G209G218 G201G217
8HD11311517.74G210G208G211G213
9HD123831−10.139G211G213G218G217
10HD1322538.666G211G213G219G217
11HD143835−19.468G214G211G217G209
12KR133008−14.873G219G218G217G209
13KR14374816.539G219G208G217G212
14WM113008−0.423G211G213G217G218
15WM12349210.835G204G220G203G217
16WM133153−0.487G211G217G220G204
17WM14361014.413G204G220G203G217
18WM153832−3.591G211G217G213G219
19ML132631−1.416G219G218G213G217
20NH112929−13.229G211G214G217G213
21NH122565−2.111G217G214G219G208
22NH1325415.132G219G217G211G213
23NH14354514.732G219G204G218G217
24NH153065−13.566G214G217G211G210
§ Dongola 2011 (DN11), Dongola 2012 (DN12), Dongola 2013 (DN13), Dongola 2014 (DN14), Dongola 2015 (DN15), Hamdab 2014 (HM14), Hamdab 2015 (HM15), Hudeiba 2011 (HD11), Hudeiba 2012 (HD12), Hudeiba 2013 (HD13), Hudeiba 2014 (HD14), Wad Medani 2011 (WM11), Wad Medani 2012 (WM12), Wad Medani 2013 (WM13), Wad Medani 2014 (WM14), Wad Medani 2015 (WM15), Al-Kumur 2013 (KR13), Al-Kumur 2014 (KR14), Al-Managil 2013 (ML13), New Halfa 2011 (NH11), New Halfa 2012 (NH12), New Halfa 2013 (NH13), New Halfa 2014 (NH14), and New Halfa 2015 (NH15).
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MDPI and ACS Style

Tahir, I.S.A.; Elbashier, E.M.E.; Mustafa, H.M.; Elhashimi, A.M.A.; Abdalla, M.G.A.; Hassan, M.K.; Saad, A.S.I.; Elbashir, A.A.E.; Elsheikh, O.; Meheesi, S. Durum Wheat Field Performance and Stability in the Irrigated, Dry and Heat-Prone Environments of Sudan. Agronomy 2023, 13, 1598. https://doi.org/10.3390/agronomy13061598

AMA Style

Tahir ISA, Elbashier EME, Mustafa HM, Elhashimi AMA, Abdalla MGA, Hassan MK, Saad ASI, Elbashir AAE, Elsheikh O, Meheesi S. Durum Wheat Field Performance and Stability in the Irrigated, Dry and Heat-Prone Environments of Sudan. Agronomy. 2023; 13(6):1598. https://doi.org/10.3390/agronomy13061598

Chicago/Turabian Style

Tahir, Izzat S. A., Elfadil M. E. Elbashier, Hala M. Mustafa, Ashraf M. A. Elhashimi, Modather G. A. Abdalla, Mohamed K. Hassan, Abu Sefyan I. Saad, Awad A. E. Elbashir, Omer Elsheikh, and Sara Meheesi. 2023. "Durum Wheat Field Performance and Stability in the Irrigated, Dry and Heat-Prone Environments of Sudan" Agronomy 13, no. 6: 1598. https://doi.org/10.3390/agronomy13061598

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